{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用户和活动关联关系处理\n",
    "\n",
    "\n",
    "整个数据集中活动数目（events.csv）太多，所以下面的处理我们找出只在训练集和测试集中出现的活动和用户集合，并对他们重新编制索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存数据\n",
    "import pickle\n",
    "\n",
    "import itertools\n",
    "\n",
    "#处理事件字符串\n",
    "import datetime\n",
    "\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import scipy.sparse as ss\n",
    "\n",
    "#相似度/距离\n",
    "import scipy.spatial.distance as ssd\n",
    "\n",
    "from collections import defaultdict\n",
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user,event,invited,timestamp,interested,not_interested\n",
      "user,event,invited,timestamp\n",
      "number of uniqueUsers :3391\n",
      "number of uniqueEvents :13418\n"
     ]
    }
   ],
   "source": [
    " \"\"\"\n",
    "我们只关心train和test中出现的user和event，因此重点处理这部分关联数据\n",
    "\n",
    "train.csv 有6列：\n",
    "user：用户ID\n",
    "event：活动ID\n",
    "invited：是否被邀请（0/1）\n",
    "timestamp：ISO-8601 UTC格式时间字符串，表示用户看到该活动的时间\n",
    "interested, and not_interested\n",
    "\n",
    "Test.csv 除了没有interested, and not_interested，其余列与train相同\n",
    " \"\"\"\n",
    "    \n",
    "# 统计训练集中有多少不同的用户的events\n",
    "uniqueUsers = set()\n",
    "uniqueEvents = set()\n",
    "\n",
    "#倒排表\n",
    "#统计每个用户参加的活动   / 每个活动参加的用户\n",
    "eventsForUser = defaultdict(set)\n",
    "usersForEvent = defaultdict(set)\n",
    "    \n",
    "for filename in [\"train.csv\", \"test.csv\"]:\n",
    "    f = open(filename, 'rb')\n",
    "    \n",
    "    #忽略第一行（列名字）\n",
    "    f1 = f.readline()\n",
    "    f2 = f1.strip().decode()\n",
    "    print(f2)\n",
    "    f3 = f2.split(\",\")\n",
    "#     f.readline().strip().split(',')\n",
    "    \n",
    "    for line in f:    #对每条记录\n",
    "        cols = line.strip().decode().split(\",\")\n",
    "        uniqueUsers.add(cols[0])   #第一列为用户ID\n",
    "        uniqueEvents.add(cols[1])   #第二列为活动ID\n",
    "        \n",
    "        #eventsForUser[cols[0]].add(cols[1])    #该用户参加了这个活动\n",
    "        #usersForEvent[cols[1]].add(cols[0])    #该活动被用户参加\n",
    "    f.close()\n",
    "\n",
    "\n",
    "n_uniqueUsers = len(uniqueUsers)\n",
    "n_uniqueEvents = len(uniqueEvents)\n",
    "\n",
    "print(\"number of uniqueUsers :%d\" % n_uniqueUsers)\n",
    "print(\"number of uniqueEvents :%d\" % n_uniqueEvents)\n",
    "\n",
    "#用户关系矩阵表，可用于后续LFM/SVD++处理的输入\n",
    "#这是一个稀疏矩阵，记录用户对活动感兴趣\n",
    "userEventScores = ss.dok_matrix((n_uniqueUsers, n_uniqueEvents))\n",
    "\n",
    "userIndex = dict()\n",
    "eventIndex = dict()\n",
    "\n",
    "#重新编码用户索引字典\n",
    "for i, u in enumerate(uniqueUsers):\n",
    "    userIndex[u] = i\n",
    "    \n",
    "#重新编码活动索引字典    \n",
    "for i, e in enumerate(uniqueEvents):\n",
    "    eventIndex[e] = i\n",
    "\n",
    "n_records = 0\n",
    "ftrain = open(\"train.csv\", 'rb')\n",
    "ftrain.readline()\n",
    "for line in ftrain:\n",
    "    cols = line.strip().decode().split(\",\")\n",
    "    i = userIndex[cols[0]]  #用户\n",
    "    j = eventIndex[cols[1]] #活动\n",
    "    \n",
    "    eventsForUser[i].add(j)    #该用户参加了这个活动\n",
    "    usersForEvent[j].add(i)    #该活动被用户参加\n",
    "        \n",
    "    #userEventScores[i, j] = int(cols[4]) - int(cols[5])   #interested - not_interested\n",
    "    score = int(cols[4])\n",
    "    #if score == 0:  #0在稀疏矩阵中表示该元素不存在，因此借用-1表示interested=0\n",
    "    #userEventScores[i, j] = -1\n",
    "    #else:\n",
    "    userEventScores[i, j] = score\n",
    "#     print(str(i) + \"_\" + str(j) + \"_\" + str(score))\n",
    "ftrain.close()\n",
    "\n",
    "# print (eventsForUser)\n",
    "##统计每个用户参加的活动，后续用于将用户朋友参加的活动影响到用户\n",
    "pickle.dump(eventsForUser, open(\"PE_eventsForUser.pkl\", 'wb'))\n",
    "##统计活动参加的用户\n",
    "pickle.dump(usersForEvent, open(\"PE_usersForEvent.pkl\", 'wb'))\n",
    "\n",
    "#保存用户-活动关系矩阵R，以备后用\n",
    "sio.mmwrite(\"PE_userEventScores\", userEventScores)\n",
    "\n",
    "\n",
    "#保存用户索引表\n",
    "pickle.dump(userIndex, open(\"PE_userIndex.pkl\", 'wb'))\n",
    "#保存活动索引表\n",
    "pickle.dump(eventIndex, open(\"PE_eventIndex.pkl\", 'wb'))\n",
    "\n",
    "    \n",
    "# 为了防止不必要的计算，我们找出来所有关联的用户 或者 关联的event\n",
    "# 所谓的关联用户，指的是至少在同一个event上有行为的用户pair\n",
    "# 关联的event指的是至少同一个user有行为的event pair\n",
    "uniqueUserPairs = set()\n",
    "uniqueEventPairs = set()\n",
    "for event in uniqueEvents:\n",
    "    i = eventIndex[event]\n",
    "    users = usersForEvent[i]\n",
    "    if len(users) > 2:\n",
    "        uniqueUserPairs.update(itertools.combinations(users, 2))\n",
    "        \n",
    "for user in uniqueUsers:\n",
    "    u = userIndex[user]\n",
    "    events = eventsForUser[u]\n",
    "    if len(events) > 2:\n",
    "        uniqueEventPairs.update(itertools.combinations(events, 2))\n",
    " \n",
    "#保存用户-事件关系对索引表\n",
    "pickle.dump(uniqueUserPairs, open(\"FE_uniqueUserPairs.pkl\", 'wb'))\n",
    "pickle.dump(uniqueEventPairs, open(\"PE_uniqueEventPairs.pkl\", 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 下列为生成picked_event.csv的代码，csv已生成，所以注释掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#训练集和测试集中出现的用户数目和事件数目远小于users.csv出现的用户数和events.csv出现的事件数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 引入event数据 查找在test和train中出现过的时间，并保存为新的df\n",
    "# \"\"\"\n",
    "# 活动描述信息在events.csv文件：共110维特征\n",
    "# 前9列：event_id, user_id, start_time, city, state, zip, country, lat, and lng.\n",
    "# event_id：id of the event, \n",
    "# user_id：id of the user who created the event.  \n",
    "# city, state, zip, and country： more details about the location of the venue (if known).\n",
    "# lat and lng： floats（latitude and longitude coordinates of the venue）\n",
    "# start_time： 字符串，ISO-8601 UTC time，表示活动开始时间\n",
    "\n",
    "# 后101列为词频：count_1, count_2, ..., count_100，count_other\n",
    "# count_N：活动描述出现第N个词的次数\n",
    "# count_other：除了最常用的100个词之外的其余词出现的次数\n",
    "# \"\"\"\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#读取数据\n",
    "# events = pd.read_csv(\"events.csv\")\n",
    "# events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# picked_events = events.loc[events['event_id'].isin(uniqueEvents)]\n",
    "# train_csv = pd.read_csv(\"train.csv\")\n",
    "# print(train_csv.columns)\n",
    "\n",
    "# # picked_events[\"interest\"] = 0\n",
    "\n",
    "# for indexs in train_csv.index:\n",
    "#     userID =  train_csv.loc[indexs][\"user\"]\n",
    "#     eventID =  train_csv.loc[indexs][\"event\"]\n",
    "# #     check in event \n",
    "#     print(userID)\n",
    "#     print(eventID)\n",
    "# #     train_csv['user']== userID\n",
    "#     print(picked_events[np.logical_and(True,picked_events['event_id']==eventID)])\n",
    "    \n",
    "# pick frome event for train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# picked_events.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# picked_events.to_csv(\"picked_events.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取涉及训练和测试集的event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "picked_events = pd.read_csv(\"picked_events.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13418 entries, 0 to 13417\n",
      "Columns: 111 entries, Unnamed: 0 to c_other\n",
      "dtypes: float64(2), int64(104), object(5)\n",
      "memory usage: 11.4+ MB\n"
     ]
    }
   ],
   "source": [
    "picked_events.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>event_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>c_1</th>\n",
       "      <th>c_2</th>\n",
       "      <th>c_3</th>\n",
       "      <th>c_4</th>\n",
       "      <th>c_5</th>\n",
       "      <th>...</th>\n",
       "      <th>c_92</th>\n",
       "      <th>c_93</th>\n",
       "      <th>c_94</th>\n",
       "      <th>c_95</th>\n",
       "      <th>c_96</th>\n",
       "      <th>c_97</th>\n",
       "      <th>c_98</th>\n",
       "      <th>c_99</th>\n",
       "      <th>c_100</th>\n",
       "      <th>c_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.341800e+04</td>\n",
       "      <td>1.341800e+04</td>\n",
       "      <td>1.341800e+04</td>\n",
       "      <td>8062.000000</td>\n",
       "      <td>8062.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "      <td>13418.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.477611e+06</td>\n",
       "      <td>2.140873e+09</td>\n",
       "      <td>2.134713e+09</td>\n",
       "      <td>25.727517</td>\n",
       "      <td>-24.807209</td>\n",
       "      <td>2.359964</td>\n",
       "      <td>1.464972</td>\n",
       "      <td>1.323372</td>\n",
       "      <td>0.888732</td>\n",
       "      <td>1.159711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064913</td>\n",
       "      <td>0.083992</td>\n",
       "      <td>0.093755</td>\n",
       "      <td>0.070502</td>\n",
       "      <td>0.082427</td>\n",
       "      <td>0.233790</td>\n",
       "      <td>0.082874</td>\n",
       "      <td>0.076837</td>\n",
       "      <td>0.073558</td>\n",
       "      <td>57.554777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.441290e+05</td>\n",
       "      <td>1.232469e+09</td>\n",
       "      <td>1.254357e+09</td>\n",
       "      <td>21.162472</td>\n",
       "      <td>91.900619</td>\n",
       "      <td>19.331141</td>\n",
       "      <td>2.959769</td>\n",
       "      <td>2.720104</td>\n",
       "      <td>1.972209</td>\n",
       "      <td>15.695718</td>\n",
       "      <td>...</td>\n",
       "      <td>0.309890</td>\n",
       "      <td>0.377730</td>\n",
       "      <td>0.388404</td>\n",
       "      <td>0.312148</td>\n",
       "      <td>0.503164</td>\n",
       "      <td>15.553234</td>\n",
       "      <td>0.356777</td>\n",
       "      <td>0.455338</td>\n",
       "      <td>0.337954</td>\n",
       "      <td>110.916584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.040700e+05</td>\n",
       "      <td>1.329876e+06</td>\n",
       "      <td>-86.151000</td>\n",
       "      <td>-157.991000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.539075e+05</td>\n",
       "      <td>1.081551e+09</td>\n",
       "      <td>1.027696e+09</td>\n",
       "      <td>3.608000</td>\n",
       "      <td>-96.886500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.475696e+06</td>\n",
       "      <td>2.122509e+09</td>\n",
       "      <td>2.150758e+09</td>\n",
       "      <td>34.040000</td>\n",
       "      <td>-76.794000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.290948e+06</td>\n",
       "      <td>3.206782e+09</td>\n",
       "      <td>3.220623e+09</td>\n",
       "      <td>42.983750</td>\n",
       "      <td>98.656750</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.137701e+06</td>\n",
       "      <td>4.294677e+09</td>\n",
       "      <td>4.294033e+09</td>\n",
       "      <td>61.498000</td>\n",
       "      <td>174.777000</td>\n",
       "      <td>2186.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1801.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1801.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9664.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0      event_id       user_id          lat          lng  \\\n",
       "count  1.341800e+04  1.341800e+04  1.341800e+04  8062.000000  8062.000000   \n",
       "mean   1.477611e+06  2.140873e+09  2.134713e+09    25.727517   -24.807209   \n",
       "std    9.441290e+05  1.232469e+09  1.254357e+09    21.162472    91.900619   \n",
       "min    0.000000e+00  1.040700e+05  1.329876e+06   -86.151000  -157.991000   \n",
       "25%    6.539075e+05  1.081551e+09  1.027696e+09     3.608000   -96.886500   \n",
       "50%    1.475696e+06  2.122509e+09  2.150758e+09    34.040000   -76.794000   \n",
       "75%    2.290948e+06  3.206782e+09  3.220623e+09    42.983750    98.656750   \n",
       "max    3.137701e+06  4.294677e+09  4.294033e+09    61.498000   174.777000   \n",
       "\n",
       "                c_1           c_2           c_3           c_4           c_5  \\\n",
       "count  13418.000000  13418.000000  13418.000000  13418.000000  13418.000000   \n",
       "mean       2.359964      1.464972      1.323372      0.888732      1.159711   \n",
       "std       19.331141      2.959769      2.720104      1.972209     15.695718   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        1.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        3.000000      2.000000      2.000000      1.000000      1.000000   \n",
       "max     2186.000000     82.000000     85.000000     71.000000   1801.000000   \n",
       "\n",
       "           ...               c_92          c_93          c_94          c_95  \\\n",
       "count      ...       13418.000000  13418.000000  13418.000000  13418.000000   \n",
       "mean       ...           0.064913      0.083992      0.093755      0.070502   \n",
       "std        ...           0.309890      0.377730      0.388404      0.312148   \n",
       "min        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "25%        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "50%        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "75%        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "max        ...           7.000000      9.000000     10.000000      9.000000   \n",
       "\n",
       "               c_96          c_97          c_98          c_99         c_100  \\\n",
       "count  13418.000000  13418.000000  13418.000000  13418.000000  13418.000000   \n",
       "mean       0.082427      0.233790      0.082874      0.076837      0.073558   \n",
       "std        0.503164     15.553234      0.356777      0.455338      0.337954   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max       23.000000   1801.000000      9.000000     16.000000      7.000000   \n",
       "\n",
       "            c_other  \n",
       "count  13418.000000  \n",
       "mean      57.554777  \n",
       "std      110.916584  \n",
       "min        0.000000  \n",
       "25%       14.000000  \n",
       "50%       38.000000  \n",
       "75%       75.000000  \n",
       "max     9664.000000  \n",
       "\n",
       "[8 rows x 106 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "picked_events.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn import svm\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 聚类CH测试函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 一个参数点（聚类数据为K）的模型，在校验集上评价聚类算法性能\n",
    "def K_cluster_analysis(K, X_train, y_train, X_val, y_val):\n",
    "    start = time.time()\n",
    "    \n",
    "    print(\"K-means begin with clusters: {}\".format(K));\n",
    "    \n",
    "    #K-means,在训练集上训练\n",
    "    mb_kmeans = KMeans(n_clusters = K)\n",
    "#     MiniBatch\n",
    "    mb_kmeans.fit(X_train)\n",
    "    \n",
    "    # 在训练集和测试集上测试\n",
    "    #y_train_pred = mb_kmeans.fit_predict(X_train)\n",
    "    y_val_pred = mb_kmeans.predict(X_val)\n",
    "    \n",
    "    #以前两维特征打印训练数据的分类结果\n",
    "    #plt.scatter(X_train[:, 0], X_train[:, 1], c=y_pred)\n",
    "    #plt.show()\n",
    "\n",
    "    # K值的评估标准\n",
    "    #常见的方法有轮廓系数Silhouette Coefficient和Calinski-Harabasz Index\n",
    "    #这两个分数值越大则聚类效果越好\n",
    "    CH_score = metrics.calinski_harabaz_score(X_train,mb_kmeans.predict(X_train))\n",
    "#     CH_score = metrics.silhouette_score(X_train,mb_kmeans.predict(X_train))\n",
    "    \n",
    "    #也可以在校验集上评估K\n",
    "#     v_score = metrics.v_measure_score(y_val, y_val_pred)\n",
    "    \n",
    "    end = time.time()\n",
    "    print(\"CH_score: {}, time elaps:{}\".format(CH_score, int(end-start)))\n",
    "#     print(\"v_score: {}\".format(v_score))\n",
    "    \n",
    "    return CH_score\n",
    "# ,v_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 取c_1到c_100和c_other组成聚类数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_n = []\n",
    "for i in range(1, 101):\n",
    "    c_n.append(\"c_\" + str(i))\n",
    "c_n.append(\"c_other\")\n",
    "\n",
    "X_train = picked_events[c_n]\n",
    "\n",
    "# train_csv = pd.read_csv(\"train.csv\")\n",
    "\n",
    "# make userID to be a column to the train, and interest to be y\n",
    "\n",
    "# y_train为临时取值，没有实际意义，只是为了使用split函数\n",
    "y_train = picked_events['event_id']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Users\\SEELE\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "X_train_part, X_val, y_train_part, y_val = train_test_split(X_train,y_train, train_size = 0.8,random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 通过10到100，10到20的区间逐步筛选最佳聚类K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-means begin with clusters: 12\n",
      "CH_score: 65132.65787220053, time elaps:1\n",
      "K-means begin with clusters: 13\n",
      "CH_score: 66708.40826310334, time elaps:1\n",
      "K-means begin with clusters: 14\n",
      "CH_score: 67486.13556054948, time elaps:2\n",
      "K-means begin with clusters: 15\n",
      "CH_score: 67354.63060433631, time elaps:2\n",
      "K-means begin with clusters: 16\n",
      "CH_score: 66608.54245189163, time elaps:1\n",
      "K-means begin with clusters: 17\n",
      "CH_score: 65295.480930222584, time elaps:2\n",
      "K-means begin with clusters: 18\n",
      "CH_score: 64022.312279419275, time elaps:2\n",
      "K-means begin with clusters: 19\n",
      "CH_score: 63143.99203044726, time elaps:2\n",
      "K-means begin with clusters: 20\n",
      "CH_score: 62474.28953998861, time elaps:2\n",
      "K-means begin with clusters: 21\n",
      "CH_score: 60881.7556389214, time elaps:2\n",
      "K-means begin with clusters: 22\n",
      "CH_score: 59839.89147670138, time elaps:2\n"
     ]
    }
   ],
   "source": [
    "Ks = [12,13,14,15,16,17,18,19,20,21,22]\n",
    "CH_scores = []\n",
    "# v_scores = []\n",
    "for K in Ks:\n",
    "    ch = K_cluster_analysis(K, X_train_part, y_train_part, X_val, y_val)\n",
    "    CH_scores.append(ch)\n",
    "#     v_scores.append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xe35c048>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0d2zlyrwjMls3ZXeX/aOP0jhD+/ap97DBBnlHZK3NyJFp99Yf/Sht+33DDWlthFkpKbvkcM45MHcu/PrX0LNnw/XN1sVll6UE8dOfwuabp2nSZqWkqNtKkjpLmixpgaQqSbtLulzS65LmZo8DC+pfJKla0iuShhSUD83KqiWNKijfVtIMSQslTcqOI210n3wCf/sbnHsuHHRQU/wTzBIJRo9Ot5kuvjiNb5mVkmJ7DqOBxyPi0OyLeyNgCHBTRNxQWFFSX+AIYEdga+B/JW2fvX0rsB9QA8ySNDUiXgZ+kn3WREm3AycCY9azbXVssEHqMfg+sDWHNm3SgVFLl6Y9u7p0Sbc0zUpBgz0HSZ2AwcA4gIhYHhFL13LJMGBiRHwcEa8B1cCu2aM6Il6NiOXARGCYJAH7AJOz68cDw9e1QQ1p08brGaz5bLABPPgg7LEHHHMMPPFE3hGZFaeY20rbAbXA3ZLmSBorqWP23hmS5km6S1KXrGwbYFHB9TVZ2ZrKNwOWRsSK1crNWoWNNko91r594TvfgenT847IrGHFJId2wEBgTEQMAJYBo0i3ff4N6A+8AdyY1a9vXkasQ3kdkk6WVCmpsra2tojQzVqGzp3h8cfT4VEHHpg2eTRryYpJDjVATUTMyF5PBgZGxN8j4tOIWAncSbpttKp+4TygHsDitZS/DXSW1G618joi4o6IqIiIim7duhURulnLseWW8OSTabuW/feH117LOyKzNWswOUTEm8AiSX2yon2BlyUVHqD5HWDVb6GpwBGSOkjaFugNzARmAb2zmUntSYPWUyMigKeBQ7PrRwBT1rNdZi3SttumcYePPkoJ4u9/zzsis/oVu0J6JHCvpHmk20jXANdJeikr2xs4GyAi5gMPAC8DjwOnZz2MFcAZwDSgCnggqwtwIXCOpGrSGMS4RmmdWQu0005pq+/Fi9NOrkvXNr3DLCeKEj0lvaKiIiorK/MOw2ydTZuWzoHYbbf0fKON8o7IyoGk2RFR0VC9stxbyawlGDIk7Qj8xz+m9Q+ffJJ3RGb/4uRglqPDD4cxY9JtpuOP9wJNazm8HMwsZ6eckvZhuuSStFHf6NHeqM/y5+Rg1gJcdBG8/TbcdFPaqO/SS/OOyMqdk4NZCyClrb2XLEk7unbtCmeckXdUVs6cHMxaiDZtYOxYePfddCZE165pV1ezPHhA2qwFadcOJk2CvfaCESPg0UfzjsjKlZODWQuz4YYwdSr06weHHgp/+EPeEVk5cnIwa4E6dYLHHkunFR50ELz4Yt4RWblxcjBrobp3T/swbbJJWjBXXZ13RFZOnBzMWrAvfSkliBUrYL/90n5MZs3BycGshdthh3SL6e23Uw9iyZK8I7Jy4ORgVgJ22QV+9Sv485/TGMSyZXlHZK2dk4NZidh3X7j/fpgxA777XVi+PO+IrDVzcjArIYccAnfckbb4Pu44+PTTvCOy1sorpM1KzIknpnGHCy6ALl3gttu8UZ81PicHsxJ0/vlpgPq662CzzeCqq/KOyFqbom4rSeosabKkBZKqJO1e8N55kkLS5tlrSbpFUrWkeZIGFtQdIWlh9hhRUL5zduRodXatfweZNeDaa+Gkk+Dqq9NurmaNqdiew2jg8Yg4VFJ7YCMAST2B/YD/K6h7ANA7e+wGjAF2k9QVuAyoAAKYLWlqRLyb1TkZmA48CgwFHlvPtpm1ahLcfnvaqO+cc1IP4rjj8o7KWosGew6SOgGDgXEAEbE8IlYdiX4TcAHpy36VYcCESKYDnSVtBQwBnoyIJVlCeBIYmr3XKSKej3Sg9QRgeCO1z6xVa9sW7r03zWQ64QS45RYo0WPhrYUp5rbSdkAtcLekOZLGSuoo6WDg9YhYfdeXbYBFBa9rsrK1ldfUU16HpJMlVUqqrK2tLSJ0s9avQwd4+GE48EA488w0zfXdd/OOykpdMcmhHTAQGBMRA4BlwOXAJUB951XVN14Q61BetzDijoioiIiKbt26FRG6WXnYZBOYMgV++lP49a9h4ECYOTPvqKyUFZMcaoCaiJiRvZ5MShbbAi9K+ivQA3hB0pZZ/Z4F1/cAFjdQ3qOecjP7HCQ4++y0xXcE7LFHSha+zWTrosHkEBFvAosk9cmK9gVeiIjuEdErInqRvuAHZnWnAsdls5YGAe9FxBvANGB/SV0kdQH2B6Zl770vaVA2S+k4YEpjN9SsXOy2G8yZk7bZOPdcGDbM+zHZ51fsCumRwL2S5gH9gWvWUvdR4FWgGrgTOA0gIpYAVwKzsscVWRnAqcDY7Jq/4JlKZuulSxd46CEYPRoefxz694fnn887KislihLtc1ZUVERlZWXeYZi1eJWVcPjh8Le/wTXXwHnnpfOqrTxJmh0RFQ3V818Rs1auogJeeAG+8x248MJ0u+ntt/OOylo6JwezMrDppvDAA3DrrfDUU+k207PP5h2VtWRODmZlQoLTToPp0+ELX4C99063mVauzDsya4mcHMzKzIABMHs2HHYYXHIJHHAAvPVW3lFZS+PkYFaGOnWC++5LZ0P8/vfpNtPvfpd3VNaSODmYlSkJvv/9dLJcp05pf6YrrvABQpY4OZiVuX790nTXo46Cyy6DIUPgzTfzjsry5uRgZmy8MUyYAOPGwXPPpdtMTz2Vd1SWJycHMwPSbaYTToBZs6BrV9hvP7j0Ut9mKldODmb2GTvumBLEiBFw5ZVpLGKxt8IsO04OZlZHx45w990wfnxKFP37w7RpeUdlzcnJwczW6Ljj0mD1FlvA0KFw8cWwYkXeUVlzcHIws7XaYYc03fWkk+DHP04rq2tqGr7OSpuTg5k1aKON4M4703nVc+em20yPPpp3VNaUnBzMrGhHHZW23ujRA771LbjgAvjkk7yjsqbg5GBmn8v226fN+37wA7j+ehg8GP7617yjssbm5GBmn9uGG8KYMTBpEsyfD337wuWXw7JleUdmjaWo5CCps6TJkhZIqpK0u6QrJc2TNFfSE5K2zupK0i2SqrP3BxZ8zghJC7PHiILynSW9lF1zS3aWtJm1cN/7Hrz0Enz72/CjH0GfPvA//+NtwFuDYnsOo4HHI+LLwFeBKuD6iOgXEf2B3wCXZnUPAHpnj5OBMQCSugKXAbsBuwKXSeqSXTMmq7vquqHr2S4zayZf+lLqQTz7LGy1FRx7LAwalLbhsNLVYHKQ1AkYDIwDiIjlEbE0Iv5RUK0jsOow6mHAhEimA50lbQUMAZ6MiCUR8S7wJDA0e69TRDwf6UDrCcDwxmqgmTWPPfdMU17Hj4fXX4c99oAjj0xnV1vpKabnsB1QC9wtaY6ksZI6Aki6WtIi4Gj+1XPYBlhUcH1NVra28pp6yuuQdLKkSkmVtbW1RYRuZs2pTZu0cO7Pf077Mk2ZAl/+MvzXf8E//5l3dPZ5FJMc2gEDgTERMQBYBowCiIhLIqIncC9wRla/vvGCWIfyuoURd0RERURUdOvWrYjQzSwPHTumMYhXXoHvfheuvhp6905bcng8ojQUkxxqgJqImJG9nkxKFoXuA75bUL9nwXs9gMUNlPeop9zMSlzPnmmAevp06NUr7fq6yy7p9Dlr2RpMDhHxJrBIUp+saF/gZUm9C6odDCzInk8FjstmLQ0C3ouIN4BpwP6SumQD0fsD07L33pc0KJuldBwwpVFaZ2Ytwm67pQHq++6D2lrYay849FB49dW8I7M1KXa20kjgXknzgP7ANcC1kv6Ule0PnJnVfRR4FagG7gROA4iIJcCVwKzscUVWBnAqMDa75i/AY+vZLjNrYaQ0QL1gQdoK/LHH0r5NF14I//hHw9db81KaIFR6KioqorKyMu8wzGwdLV6cdnkdPx66d4errkq3ndq2zTuy1k3S7IioaKieV0ibWS623hruuSedF7H99nDyyTBwIPz2t3lHZuDkYGY5q6hIA9QPPphuL+27LwwfDgsX5h1ZeXNyMLPcSWmAuqoqnRnx1FPpuNJzz4WlS/OOrjw5OZhZi7HhhjBqVOo1jBgBN90E//7vcNttPoGuuTk5mFmLs+WW6XChF16Ar3wFTj8dvvpVn2PdnJwczKzF6t8/DVA//DB8/HE6x/pb30rTYa1pOTmYWYsmpQHq+fPhhhvgD3+AnXaC//xPeOedvKNrvZwczKwkdOiQBqirq9O011tvTedHuBfRNJwczKykdOuWBqjnzk29isMPhw8/zDuq1sfJwcxK0le+AhMmwLx5cM45eUfT+jg5mFnJOuAAOP98uP32tIjOGo+Tg5mVtKuvTru+nnQSvPZa3tG0Hk4OZlbSNtgAJk5M4w9HHAHLl+cdUevg5GBmJa9XLxg3DmbOTDu92vpzcjCzVuG734XTToMbb4RHHsk7mtLn5GBmrcaNN6ZtNkaMgNdfzzua0lZUcpDUWdJkSQskVUnaXdL12et5kh6W1Lmg/kWSqiW9ImlIQfnQrKxa0qiC8m0lzZC0UNIkSe0bt5lmVg423BAmTYKPPoKjjvJmfeuj2J7DaODxiPgy8FWgCngS2Cki+gF/Bi4CkNQXOALYERgK3CapraS2wK3AAUBf4MisLsBPgJsiojfwLnBiYzTOzMpPnz4wZkw6I+LKK/OOpnQ1mBwkdQIGA+MAImJ5RCyNiCciYlVeng70yJ4PAyZGxMcR8RrpXOhds0d1RLwaEcuBicAwSQL2ASZn148HhjdO88ysHB17bLq1dOWV8PTTeUdTmorpOWwH1AJ3S5ojaaykjqvVOQF4LHu+DbCo4L2arGxN5ZsBSwsSzapyM7N19rOfpeNHjz4a3nor72hKTzHJoR0wEBgTEQOAZUDheMElwArg3lVF9XxGrEN5HZJOllQpqbK2traI0M2sXG28MTzwACxZAscdBytX5h1RaSkmOdQANRExI3s9mZQskDQCOAg4OiKioH7Pgut7AIvXUv420FlSu9XK64iIOyKiIiIqunXrVkToZlbO+vWDm29OhwTdcEPe0ZSWBpNDRLwJLJLUJyvaF3hZ0lDgQuDgiPig4JKpwBGSOkjaFugNzARmAb2zmUntSYPWU7Ok8jRwaHb9CGBKI7TNzIxTTknnU19yCUyfnnc0pUP/+sG/lkpSf2As0B54FTie9GXfAVh13Mb0iPhBVv8S0jjECuCsiHgsKz8QuBloC9wVEVdn5duRBqi7AnOAYyLi47XFVFFREZWVlZ+rsWZWnpYuhQEDIALmzIEuXfKOKD+SZkdERYP1ikkOLZGTg5l9HjNnwh57wLe/Db/8ZdqLqRwVmxy8QtrMysKuu8K116bzqG+7Le9oWj4nBzMrG2efDQcemA4Hmjs372haNicHMysbbdrA+PGw+ebpeNH33887opbLycHMysrmm8N990F1ddrFtUSHXZuck4OZlZ299oLLLoP/+Z/Uk7C6nBzMrCxdcgnsvTecfjpUVeUdTcvj5GBmZalt29Rz6NgxjT98+GHeEbUsTg5mVra23homTICXXkozmexfnBzMrKwNHQoXXAA//zk8+GDe0bQcTg5mVvauugoGDYKTToJXX807mpbBycHMyt4GG8D996d1EIcfDsuX5x1R/pwczMyAXr1g3DiorISLLso7mvw5OZiZZQ45JE1t/elP4ZFH8o4mX04OZmYFbrgB+vdPZ1DX1OQdTX6cHMzMCmy4IUyaBB99BEcdBStWNHxNa+TkYGa2mu23h9tvh2efhSuuyDuafDg5mJnV45hj4D/+I01z/e1v846m+RWVHCR1ljRZ0gJJVZJ2l3SYpPmSVkqqWK3+RZKqJb0iaUhB+dCsrFrSqILybSXNkLRQ0qTsjGkzs1z97GfQpw8cfTS89Vbe0TSvYnsOo4HHI+LLwFeBKuBPwCHA7wsrSuoLHAHsCAwFbpPUVlJb4FbgAKAvcGRWF+AnwE0R0Rt4FzhxvVplZtYIOnZM4w9Ll8Kxx8LKlXlH1HwaTA6SOgGDgXEAEbE8IpZGRFVEvFLPJcOAiRHxcUS8BlQDu2aP6oh4NSKWAxOBYZIE7ANMzq4fDwxf34aZmTWGfv3g5pvhiSfg+uvzjqb5FNNz2A6oBe6WNEfSWEkd11J/G2BRweuarGxN5ZsBSyNixWrlZmYtwsknw2GHpW2+n3su72iaRzHJoR2igWegAAAJK0lEQVQwEBgTEQOAZcCotdRXPWWxDuV1P1g6WVKlpMra2tq1R21m1kgkuPNO+OIX4cgjYcmSvCNqesUkhxqgJiJmZK8nk5LF2ur3LHjdA1i8lvK3gc6S2q1WXkdE3BERFRFR0a1btyJCNzNrHJtumsYf3ngDTjyx9R8v2mByiIg3gUWS+mRF+wIvr+WSqcARkjpI2hboDcwEZgG9s5lJ7UmD1lMjIoCngUOz60cAU9apNWZmTWiXXeDaa+FXv4If/zjvaJpWu4arADASuDf7Un8VOF7Sd4D/BroBj0iaGxFDImK+pAdICWQFcHpEfAog6QxgGtAWuCsi5meffyEwUdJVwByywW8zs5bm7LNh1qw0/lBbCzfemHZzbW0UJdo3qqioiMrKyrzDMLMy9OmncM45cMstaaB6woS07UYpkDQ7Iioaqldsz8HMzDJt26bprV/8Ipx3Hrz5ZrrV1LVr3pE1nlbYGTIza3oSnHtuOiRoxgzYc0/429/yjqrxODmYma2HI46AadNg8WLYfXeYOzfviBqHk4OZ2Xr6xjfgj39Mt5sGD4Ynn8w7ovXn5GBm1gh23BGmT0/HjR54YBqkLmVODmZmjWSbbdIZEIMHp5PkrrmmdBfLOTmYmTWiTTeFxx5L23xfcgmcdlppnibnqaxmZo2sfft0W6lnz7Si+vXX06ymjmvbsrSFcc/BzKwJtGmTtti49VZ45BHYZ5+0orpUODmYmTWh006Dhx6CefPga1+Dv/wl74iK4+RgZtbEhg1L51C/+25aCzFzZt4RNczJwcysGey+ezooaOON07qI3/wm74jWzsnBzKyZbL89PP889O2behN33JF3RGvm5GBm1oy22AJ+9zsYOhROOQV++MOWuRbCycHMrJltvDFMmZJOlLvqKjj+ePjkk7yj+iyvczAzy0G7dulc6p494fLL08Z9v/wlbLJJ3pEl7jmYmeVEgssug3Hj0mymwYPTGdUtQVHJQVJnSZMlLZBUJWl3SV0lPSlpYfZnl6yuJN0iqVrSPEkDCz5nRFZ/oaQRBeU7S3opu+YWSWr8ppqZtUwnnJBmLy1cmGY1VVXlHVHxPYfRwOMR8WXgq0AVMAp4KiJ6A09lrwEOAHpnj5OBMQCSugKXAbsBuwKXrUooWZ2TC64bun7NMjMrLUOHwjPPwEcfwR57wB/+kG88DSYHSZ2AwcA4gIhYHhFLgWHA+KzaeGB49nwYMCGS6UBnSVsBQ4AnI2JJRLwLPAkMzd7rFBHPRzrQekLBZ5mZlY2dd05TXbt3h29+M41B5KWYnsN2QC1wt6Q5ksZK6ghsERFvAGR/ds/qbwMsKri+JitbW3lNPeVmZmVn223TwUE77wyHHQajR+cTRzHJoR0wEBgTEQOAZfzrFlJ96hsviHUor/vB0smSKiVV1pbSDlZmZp/DZpvB//4vDB8OZ50F550HK1c2bwzFJIcaoCYiZmSvJ5OSxd+zW0Jkf75VUL9nwfU9gMUNlPeop7yOiLgjIioioqJbt25FhG5mVpq+8AV48EE44wy48UY46ij4+OPm++c3mBwi4k1gkaQ+WdG+wMvAVGDVjKMRwJTs+VTguGzW0iDgvey20zRgf0ldsoHo/YFp2XvvSxqUzVI6ruCzzMzKVtu2cMstcN11MGkSDBmSNu9rDsUughsJ3CupPfAqcDwpsTwg6UTg/4DDsrqPAgcC1cAHWV0iYomkK4FZWb0rImJJ9vxU4B7gC8Bj2cPMrOxJcP756QjS//gP+PrX0y2nLbds4n9utMRNPYpQUVERlZWVeYdhZtZsnn469SQmToQOHdbtMyTNjoiKhup5+wwzsxKx997p0Ry8fYaZmdXh5GBmZnU4OZiZWR1ODmZmVoeTg5mZ1eHkYGZmdTg5mJlZHU4OZmZWR8mukJZUC/xtHS/fHHi7EcMpBW5zeSi3Npdbe2H92/yliGhw59KSTQ7rQ1JlMcvHWxO3uTyUW5vLrb3QfG32bSUzM6vDycHMzOoo1+RwR94B5MBtLg/l1uZyay80U5vLcszBzMzWrlx7DmZmthatPjlIukvSW5L+VFB2vaQFkuZJelhS5zxjbGz1tbngvfMkhaTN84itKaypvZJGSnpF0nxJ1+UVX1NYw9/r/pKmS5orqVLSrnnG2Ngk9ZT0tKSq7L/pmVl5V0lPSlqY/dkl71gby1ra3OTfYa0+OZCOHx26WtmTwE4R0Q/4M3BRcwfVxO6hbpuR1BPYj3Ssa2tyD6u1V9LewDCgX0TsCNyQQ1xN6R7q/je+DvhRRPQHLs1etyYrgHMjYgdgEHC6pL7AKOCpiOgNPJW9bi3W1OYm/w5r9ckhIn4PLFmt7ImIWJG9nA70aPbAmlB9bc7cBFwAtKqBpjW091Tg2oj4OKvzVrMH1oTW0OYAOmXPNwUWN2tQTSwi3oiIF7Ln7wNVwDakHwHjs2rjgeH5RNj41tTm5vgOa/XJoQgnAI/lHURTk3Qw8HpEvJh3LM1ke+DrkmZIekbSLnkH1AzOAq6XtIjUU2ptPeL/T1IvYAAwA9giIt6A9GUKdM8vsqazWpsLNcl3WFknB0mXkLpt9+YdS1OStBFwCelWQ7loB3QhdcXPBx6QpHxDanKnAmdHRE/gbGBczvE0CUkbA78EzoqIf+QdT3NYU5ub8jusbJODpBHAQcDR0frn8/4bsC3woqS/krqgL0jaMteomlYN8FAkM4GVpD1pWrMRwEPZ8weBVjUgDSBpA9KX5L0Rsaqtf5e0Vfb+VkCruoW4hjY3+XdYWSYHSUOBC4GDI+KDvONpahHxUkR0j4heEdGL9MU5MCLezDm0pvQrYB8ASdsD7Wn9G7QtBvbKnu8DLMwxlkaX9fzGAVUR8dOCt6aSEiPZn1OaO7amsqY2N8d3WKtfBCfpfuAbpF+NfwcuI92L7QC8k1WbHhE/yCXAJlBfmyNiXMH7fwUqIqJVfFmu4b/xL4C7gP7AcuC8iPhtXjE2tjW0+RVgNOmW2kfAaRExO68YG5ukPYFngZdIPUGAi0n34B8AvkiaiXdYRNQ3IaPkrKXNt9DE32GtPjmYmdnnV5a3lczMbO2cHMzMrA4nBzMzq8PJwczM6nByMDOzOpwczMysDicHMzOrw8nBzMzq+H8ZWAplE3f/+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制不同PCA维数下模型的性能，找到最佳模型／参数（分数最高）\n",
    "plt.plot(Ks, np.array(CH_scores), 'b-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 最佳在14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(Ks, np.array(v_scores), 'g-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 降维可视化聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13418, 2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#降为2维，方便plot\n",
    "pca = PCA(n_components=2)\n",
    "\n",
    "#根据最佳参数，在全体训练数据上重新训练模型\n",
    "pca.fit(X_train)\n",
    "X_train_pca = pca.transform(X_train)\n",
    "\n",
    "X_train_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import pylab\n",
    "# pylab.rcParams['figure.figsize'] = (50.0, 50.0) # 显示大小\n",
    "\n",
    "# plt.figure(figsize=(10,5))\n",
    "#显示聚类结果\n",
    "#画出聚类结果，每一类用一种颜色\n",
    "colors = ['b','g','r','k','c','m','y','#e24fff','#524C90','#845868','#e24f0f','#524090','#845f68','#8f5868']\n",
    "\n",
    "\n",
    "\n",
    "n_clusters = 14\n",
    "mb_kmeans = KMeans(n_clusters = n_clusters)\n",
    "mb_kmeans.fit(X_train)\n",
    "\n",
    "y_train_pred = mb_kmeans.labels_\n",
    "cents = mb_kmeans.cluster_centers_#质心\n",
    "\n",
    "for i in range(n_clusters):\n",
    "    index = np.nonzero(y_train_pred==i)[0]\n",
    "\n",
    "    x1 = X_train_pca[index,0]\n",
    "    x2 = X_train_pca[index,1]\n",
    "    y_i = y_train_pred[index]\n",
    "#     print(x1)\n",
    "#     print(x2)\n",
    "#     print(y_i)\n",
    "#     print(\"--------------------------------------------------------------------------------\")\n",
    "    if len(index) > 200:\n",
    "        for j in range(len(x1)):\n",
    "            if j < 20:  #每类打印20个\n",
    "                plt.text(x1[j],x2[j],str(int(y_i[j])),color=colors[i],\\\n",
    "                    fontdict={'weight': 'bold', 'size': 9})\n",
    "    #plt.scatter(cents[i,0],cents[i,1],marker='x',color=colors[i],linewidths=12)\n",
    "\n",
    "plt.axis([-60,200,-50,30])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 尝试一些其他的多维可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Users\\SEELE\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: 'pandas.tools.plotting.radviz' is deprecated, import 'pandas.plotting.radviz' instead.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe4ce5c0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = pd.DataFrame(y_train_pred, columns = [\"class\"])\n",
    "data = X_train.join(y)\n",
    "\n",
    "from pandas.tools.plotting import radviz\n",
    "plt.figure()\n",
    "radviz(data, 'class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Users\\SEELE\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: 'pandas.tools.plotting.andrews_curves' is deprecated, import 'pandas.plotting.andrews_curves' instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xfea2c18>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import andrews_curves\n",
    "plt.figure()\n",
    "andrews_curves(data, 'class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 关于聚类的应用想法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将事件进行分类，得到14个分类，然后可以将事件进行onehot编码，用作事件的特征。\n",
    "已知用户数据表，可将事件的onehot编码和用户数据依据eventID和userID进行拼接，拼接成新的训练集和测试集，以备后续训练及预测。\n",
    "个人理解，聚类是一种包含特征分类含义的降维。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
